Understanding the level of measurement of variables is crucial in statistical analysis as it determines the appropriate methods for analyzing data.
The four main levels of measurement – nominal, ordinal, interval, and ratio – offer varying levels of detail, with nominal providing the least and interval and ratio offering the most.
This guide is designed for those interested in gaining a basic understanding of nominal data. We will define what nominal data is, examine its characteristics, provide examples, and explain how to analyze it.
Additionally, we will compare and contrast nominal data with ordinal data.
Data Nominal Definition
Nominal data is a type of data that represents categories or labels. It is used to label or name an item or attribute and cannot be quantified or ordered.
Nominal data is often used to classify or categorize items or groups, such as gender, hair color, or religious affiliation. It is typically represented by non-numeric values such as strings or integers used to label categories.
It does not have a natural order, and there is no mathematical operations can be performed on it. It is the lowest level of measurement in statistics.
Data Nominal Characteristics
The characteristic of nominal data means that the position of the data is balanced and cannot overlap. In statistics, you cannot use mathematical operations or formulas, for example, in subtraction, addition, division, and multiplication.
If you find it complicated when using nominal data, you can replace it using other options, such as trying ordinal. Because object grouping is done by categorizing.
Similar research is suitable for researching a case or an exclusive survey. Also, numbers are evaluated from the highest data or the most.
Generally, you can find it when researching what will happen in the general or local elections. To see the formula, you can look it up in statistics books. But make sure to look for relevant and trusted sources.
Data Nominal Examples
For those of you who are preparing a thesis or research using statistics, don’t worry. Because there are several examples of nominal data to be used as material for a statement.
For instance, in statistical analysis, you can form a framework using the test name of the coefficient of contingency origin.
You can also use other names, such as the Ch square test and many others. Research that uses nominal data does not have permanent rules, so it is free to give titles in classifying objects.
If you are still trying to decide whether to choose it, you can make this model as a guide. For example, grouping types of mounts based on the company. “Innova” A and “Avanza” B are medium-sized cars, and “Fortuner” C is a prominent type.
Most nominal data is sorted into categories, where each response fits only into one category.
Some examples of nominal data are:
1. Which state do you live in? (Followed by a drop-down list of names of states)
2.Which among the following do you usually choose for pizza toppings?
- Extra Cheese
- Which is the most loved breed of dog?
- Doberman – 1
- Dalmatian – 2
- Labrador – 3
- German Shepherd – 4
3. Hair Color (black, brown, grey, blonde)
4. Preferred mode of Public Transport (bus, tram, train)
5. Employment Status (employed, unemployed, retired)
6. Literary Genre (comedy, tragedy, drama, epic, satire)
Analyze Nominal Data
and visualized in tables and charts. This allows for the use of descriptive statistics to calculate the data’s frequency distribution and central tendency.
The steps to analyze nominal data include:
1. Descriptive Statistics
These statistics allow you to see how your data is distributed. The most common methods used for nominal data are frequency distributions and measures of central tendency.
2. Frequency Distribution
A frequency distribution table is created to bring order to nominal data. This table clearly shows the number of responses for each category in the variable. These tables can also be visualized through graphs and charts.
3. Central Tendency
A measure of where most of the values lie. The most commonly used measures of central tendency for nominal data are the mode, as it is the most frequently appearing value in the frequency table.
4. Statistical Tests
Inferential statistics allow you to test scientific hypotheses about the data and dig deeper into what the data are conveying.
Non-parametric tests are used for nominal data because the data cannot be ordered in any meaningful way. Some examples of non-parametric tests are Chi-square test, Fisher’s exact test, and McNemar test.
Nonparametric tests, such as the Chi-square goodness of fit test and the Chi-square independence test, are commonly used to analyze nominal data.
The Chi-square goodness of fit test is used to determine whether the sample data collected is representative of the overall population. This test is typically used when data is collected through random sampling from a single population.
On the other hand, the Chi-square independence test is used to examine the relationship between two nominal variables. This test allows for the testing of hypotheses to determine if two nominal variables from a single sample are independent.
Nominal Data vs Ordinal Data
Nominal data and ordinal data are both types of categorical data, but they have some key differences.
Nominal data is a type of categorical data that can be assigned a name or label, but cannot be ordered or ranked in any meaningful way.
Examples of nominal data include gender, eye color, and religious affiliation. Nominal data only provides minimal detail about the data set, as it only classifies the data into different categories.
Ordinal data, on the other hand, is a type of categorical data that can be assigned a name or label and can also be ordered or ranked in a meaningful way.
Examples of ordinal data include education level (high school, college, graduate school), levels of customer satisfaction (very satisfied, satisfied, neutral, dissatisfied, very dissatisfied), and military ranks (private, sergeant, captain, etc.).
Ordinal data provides more detail than nominal data because it not only classifies the data into categories but also allows for the ranking of those categories.
Because ordinal data can be ranked, it can be analyzed with more advanced statistical methods than nominal data.